“Data analytics” refers to techniques for the analysis of data to draw conclusions about the data. Data analytics is used by many entities, including being used by businesses to make better business decisions, and being used by scientists to verify or disprove existing models or theories. Data analytics is different from data mining. In data mining, data miners sort through huge data sets to identify undiscovered patterns and establish hidden relationships. In contrast, data analytics is directed to deriving conclusions based on the data and the knowledge of the researcher configuring the data analytics. Data analytics may include the inspecting, cleaning, transforming, and modeling of data to highlight useful information, suggest conclusions, support decision making, and/or provide other beneficial results.
In some cases, data analytics may be performed on “real-time” data, which is data that is delivered for analysis as soon as it is collected or generated. A data analytics application that analyzes real-time data may be referred to as a real-time data analytics application. The development of an end-to-end real-time data analytics application is complicated and labor intensive. A developer of such a real-time data analytics application has to spend a significant amount of time programming the various components of the application, including having to program a data acquisition component, an analytics component, and a results dissemination component.